Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

Session-based recommendation (SBR) focuses on next-item prediction at a certain time
point. As user profiles are generally not available in this scenario, capturing the user intent …

Session-based recommendation (SBR) is a challenging task, which aims at recommending
items based on anonymous behavior sequences. Almost all the existing solutions for SBR …

Recommendation foundation model utilizes large language models (LLM) for
recommendation by converting recommendation tasks into natural language tasks. It …

Deep learning techniques have become the method of choice for researchers working on
algorithmic aspects of recommender systems. With the strongly increased interest in …

The problem of session-based recommendation aims to predict user actions based on
anonymous sessions. Previous methods model a session as a sequence and estimate user …

Much of the recent progress in sequential and session-based recommendation has been
driven by improvements in model architecture and pretraining techniques originating in the …

Top-N sequential recommendation models each user as a sequence of items interacted in
the past and aims to predict top-N ranked items that a user will likely interact in a» near …

Predicting a user's preference in a short anonymous interaction session instead of long-term
history is a challenging problem in the real-life session-based recommendation, eg, e …